Modified Social Forces Algorithm: from Pedestrian Dynamic to Metaheuristic Optimization.

This work proposes a new simple metaheuristic optimization method inspired by the social forces model used in pedestrian dynamics. The proposed model is a swarm-based model where a collective intelligence is shared among the agents, consisting of several persons or agents who walk over the search space to find the best solution. Each time a person finds a better solution, they share it with another person. The proposed model is then evaluated by implementing it to solve ten benchmark functions. The five are multimodal functions (Ackley, Rastrigin, Griewank, Bukin, and Michalewicz) while the other are unimodal (Sphere, Rosenbrock, Bohachevsky, Zakharov, and Booth). The performance is compared with five metaheuristic algorithms: particle swarm optimization, darts game optimizer, shell game optimization, marine predator algorithm, and Komodo mlipir algorithm. The simulation result shows that the proposed method is competitive enough to solve multimodal and unimodal functions. The performance is superior in solving Michalewicz and Zakharov functions but less competitive in solving the Bukin function. The results also imply that there is no single algorithm that is the best in solving all kinds of problems. [ABSTRACT FROM AUTHOR]